Lenox FromApax’s Post

21ST CENTURY HUMAN LEARNING AND PERFORMANCE . . . is regularly-interrupted with media notifications. What must Learning Specialists consider when designing and delivering instructional content and measuring performance? ------  "Media Multitasking Performance and Its Cognitive Requirements" "Media multitasking refers to the behavior of consuming multiple items or streams of information simultaneously (Ophir et al., 2009). In general, the human cognitive system is not well equipped for this type of information intake and processing. Several theoretical approaches, such as the capacity model of attention (Kahneman, 1973), the limited capacity hypothesis (Baddeley, 1969), the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP; Lang, 2000, 2006), or the Threaded Cognition Model (Salvucci & Taatgen, 2008) assume that media multitasking costs are based on limited attentional and cognitive resources. If multiple tasks need to be completed simultaneously or in short succession, the limited resources must either be divided among these tasks or, when resources cannot be divided, only one task can be conducted at a given time, resulting in rapid and repeated switching between tasks (Salvucci & Taatgen, 2008). This can result in decreased performance.The Cognitive Load Theory (CLT; Sweller, 2010) further differentiates between three types of load that consume the limited capacity of the cognitive system, specifically of the working memory. This theory has been initially introduced to describe how the limitations of working memory affect information processing in instructional contexts. The CTL differentiates between three types of load on the working memory that occur when interacting with learning material. This differentiation provides insights on why media multitasking demands can burden the cognitive system and can thus impair performance. Intrinsic load describes the inherent difficulty and complexity of a task itself. Extraneous load is unnecessary cognitive load imposed by poorly designed instructional materials or irrelevant information. Germane load describes cognitive efforts of meaningful processing. The effort of resource allocation and task-switching that comes with media multitasking can be considered extraneous load within the terminology of the CTL. High extraneous load leaves less resources for germane load and thus meaningful processing and ultimately task performance. Accordingly, research on the effects of media multitasking has shown that scrolling social media sites, texting, or background TV can impair reading speed and comprehension or attention in a lecture as well as learning gains when watching instructional videos (e.g., Armstrong & Chung, 2000; Bowman et al., 2010; Demirbilek & Talan, 2018; Dietz & Henrich, 2014; Dönmez & Akbulut, 2021; Jeong & Hwang, 2012)."

The way EdTechs currently use 𝗦𝗽𝗮𝗰𝗲𝗱 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻 often leads to "𝗳𝗮𝗸𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴"... When start ups claim their product is backed by learning science, Spaced Repetition usually is one of the go-to techniques. The idea is simple: When we hear new information we typically have forgotten roughly - 40% after 20min - 55% after 1h - 75% after 1 week To counter that, we simply repeat the input in certain intervals. Thereby we retain more information for longer periods. It works. No doubt about that. There is scientific evidence. 𝗦𝗼 𝘄𝗵𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗮𝘁𝗰𝗵? In my opinion the technique is based on an insufficient concept of what learning is about. Bold thesis, I know, but here is why: Think about the result we are optimising for with this... It’s just about memorising pieces of information. Be it historical facts, vocabulary or the structure of a project management method. De facto the process is seen as “successful” when the learner is able to repeat a certain set of words. ▶ 𝘛𝘩𝘦 𝘤𝘰𝘯𝘴𝘦𝘲𝘶𝘦𝘯𝘤𝘦: 𝘛𝘩𝘦 𝘪𝘥𝘦𝘢 𝘰𝘧 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘪𝘴 𝘳𝘦𝘥𝘶𝘤𝘦𝘥 𝘵𝘰 𝘮𝘦𝘳𝘦𝘭𝘺 𝘮𝘦𝘮𝘰𝘳𝘪𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘳𝘦𝘱𝘳𝘰𝘥𝘶𝘤𝘪𝘯𝘨 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯. Citing isolated facts may look like learning on the outside. BUT, ▶ do learners develop a profound understanding of the subject in that way? ▶ are the learnings really actionable? ▶ and are they able to make inferences based on it? I would strongly argue against that. Hence, the term fake learning. We want learners not just to repeat words. We want them to develop "integrated knowledge", as it is called in learning psychology. That means that any new input is not just stored in longterm memory — but is linked cognitively with existing information and experiences related to the given topic. Only then, knowledge ▶ becomes available when it is relevant, ▶ can be transferred to different contexts ▶ and really affects the learners’ behaviour. If we pose that as our desired learning outcome, we will quickly find that spaced repetition in itself is insufficient. 𝗦𝗼, 𝗵𝗼𝘄 𝗰𝗮𝗻 𝘄𝗲 𝘂𝘀𝗲 𝗦𝗽𝗮𝗰𝗲𝗱 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻, 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝘂𝗽𝗲𝗿𝗳𝗶𝗰𝗶𝗮𝗹? ▶ Imo, We should not see Spaced Repetition as a an instructional technique in itself, but rather as a 𝗹𝗮𝘆𝗲𝗿 on top of our learning methodology: It only determines WHEN we confront a learner with a given topic, but not HOW deliver that input. Consequently, it is usually not mutually exclusive with other techniques — so it can (and should) be supplemented with them. Constructivist learning methods, e.g. reflect that perspective of integrated knowledge much better, so the combination of both can be really powerful (▶ observational learning, encoding support, socratic teaching…). In short: Let's think of Spaced Repetition as an organisational structure not as a standalone technique! Curious to hear what you think!

Remember the scramble to adapt to mobile learning? Get ready for another learning tech shift – GenAI is on the rise! Hey everyone, remember that feeling of excitement (and maybe a little overwhelm) when smartphones and tablets hit the scene and changed how we learn? Well, get ready for another learning revolution – GenAI is here! GenAI, or Generative AI, is a powerful new technology that's poised to transform the way we create and deliver learning experiences. It's not just about fancy content creation, though. GenAI has the potential to completely transform how we work as L&D professionals. But hold on, before we jump into the deep end, let's address the elephant in the room: AI anxiety. 😰 It's natural to feel some apprehension about new technology, especially one that might seem like it could replace our jobs. Here's the good news: GenAI isn't here to take over. Instead, it's here to be our superpower. Imagine having a digital teammate that can help you with tasks like: - Analyzing data to identify learning needs - Drafting engaging learning content - Personalizing learning experiences for individual learners Pretty cool, right? So, how do we prepare ourselves for this exciting new future? Here are three key steps... 1. Build Trust and Ease Anxiety 🔦 Talk openly and honestly about GenAI. Discuss its potential benefits and challenges as a team. 🔦 Focus on how GenAI can augment our skills, not replace them. We'll still need our human expertise in areas like instructional design and building relationships with learners. 2. Level Up Your Team's Skills 🔦 Become familiar with GenAI – what it is, how it works, and how it can be applied to L&D. 🔦 There are tons of resources available to help us upskill, like online courses and workshops. 🔦 Focus on building competencies like critical thinking, data literacy, and creativity – these will be essential for working effectively with GenAI. 3. Partner with the AI Experts 🔦 Organization might already have an AI governance team in place. Connect with them to understand the company's policies and best practices for using GenAI. The road to GenAI adoption can be exciting, but it's important to take it together. P.S. Have you had a chance to play around with any GenAI tools yet? Share your experiences (good or bad) in the comments!

Learning designers seldom take learning theories literally. If design is a process, then a theory or model is its input that guides pedagogical thinking. Here is one theory that has consistently shaped my practice over the last twenty years: John Sweller’s Cogntive Load Theory. First proposed in the late '80s, CLT has continued to evolve through the 21st century, with validations, challenges, and refinements. One of the core assumptions in Sweller's conception of human cognitive architecture draws on evolutionary psychologist David Geary’s distinction between biologically primary and biologically secondary learning. Biologically primary learning includes skills like listening, speaking and recognizing faces – the things evolution has prepared us for. Biologically secondary learning, on the other hand, includes reading, writing, math, etc. – skills that require conscious effort and formal education. It also requires biologically primary knowledge. For example, we cannot solve 2x+5=15 if we are not taught how to solve for x. Sweller outlines five principles of human cognitive architecture: 1. Information Store: Chess masters recall past configurations with high accuracy, thanks to consistent practice over many years. This kind of information stored in long-term memory is what differentiates experts from novices in almost all fields. Implication: Learning strategies that are effective for experts, like ill-defined problem-solving, may harm novices who need explicit guidance (Expertise Reversal Effect). 2. Borrowing & Reorganizing: Much of what we learn is borrowed from others and reorganized based on prior knowledge. Implication: Novices perform better with worked examples, for instance. They could start by borrowing solutions before solving problems on their own. 3. Randomness as Genesis: New knowledge creation mirrors biological evolution – we try out solutions, test effectiveness and retain successful ones in memory. Others can then borrow and reorganize this new knowledge. 4. Narrow Limits of Change: The epigenetic system regulates evolutionary mutations to ensure gradual adaptation. Similarly, working memory limits the amount of novel information that we can process in one go to prevent overload. Implication: Instructional strategies should minimize extrinsic load (avoid starting with unguided practice) and optimise intrinsic load (use chunking, for instance). 5. Environmental Organizing & Linking: Working memory struggles with new information but can easily retrieve familiar knowledge from long-term memory. This means an expert doctor with vast stores of medical information can outperform a novice with stronger generic problem-solving skills. Implication: Rather than teaching generic skills, which are extremely difficult to transfer, focus on deepening subject understanding, which could address domain-specific as well as interdisciplinary thinking skills. Sweller, J. (2016). Working memory, long-term memory, and instructional design.

**Leveraging AI for Learning Assessment Tools in .NET MAUI** In today’s rapidly evolving digital landscape, the integration of artificial intelligence (AI) into application development is transforming how we create and interact with educational tools. One powerful cross-platform framework, .NET MAUI (Multi-Platform App UI), is gaining traction for its ability to build applications across multiple operative systems, but how can we pair it with AI to develop more intuitive and efficient learning assessment tools? At its core, .NET MAUI allows for streamlined development across iOS, Android, Windows, and macOS, using a single codebase. This efficiency makes it highly attractive for creating learning tools that need broad accessibility. When combined with AI, learning assessments can become smarter and more tailored to individual student needs. AI offers the potential to analyze learner data quickly, predicting tendencies, identifying weak areas, and providing real-time feedback loops. The combination of .NET MAUI’s cross-platform capabilities with AI-driven assessment can lead to more dynamic learning environments, where content and evaluations adapt in real time. This integrated approach facilitates personalized learning paths, enhances the overall user experience, and helps educators measure educational outcomes more effectively. Why does this matter? First, it enables scalability, providing consistent user experiences across devices and platforms. Second, pairing AI with .NET MAUI expands the depth of learning, enhancing both the educator’s ability to assess and the learner’s ability to interact with content. Additionally, automating the creation of tests, quizzes, and progress trackers provides educators with significant time-saving advantages while improving precision in measuring learning outcomes. The future of assessment is neither generic nor static. With AI powering the backend and .NET MAUI ensuring adaptability across platforms, we are looking at a future where learning tools are more interactive, adaptive, and insightful. As these technologies continue to evolve, professionals in education tech, application development, and learning design have a unique opportunity to shape the future of how knowledge is delivered and assessed. What’s your perspective on the role of AI and .NET MAUI in reshaping assessments? Share your thoughts in the comments!

Want to turn a traditional assignment into Project-Based Learning? This prompt will give you some ideas. ⬇️⬇️⬇️ Simply: 1. Run the prompt in any AI program. 2. Give it your normal assignment. 3. Look through the AI's ideas. 4. Revise, revise, revise. -------- Prompt for Turning a Traditional Assessment to a PBL [Role] You are an educator with a decade of in-the-classroom experience as well as a firm grounding in strong pedagogical principles. You believe in student-centered learning experiences that provide students with control. You are a follower of Daniel Pink's idea that people are motivated by autonomy, a quest for mastery, and a sense of purpose. You work those ideas into your assignments. [Instructions] I will provide you with a traditional assessment (such as a paper). You will go through the following steps, marked as [Step 1] to [Step 3]. Do not move on from one step until it is completed. [Step 1] You will ask me for the traditional assignment. I will provide it. [Step 2] You will provide 3 ideas for a Project-Based Learning assignment, based on the traditional assignment I provided you in [Step 1]. You will write these exact words, "Which one would you like me to work out in more detail? Or would you like me to generate 3 new options?" [Step 3] If I asked you to generate 3 new options, do that and move on to [Step 4]. If I asked you to give more details about one of the 3 options you've already given me, then provide me with a full outline of the assignment. This will include a full write-up of the assignment for students and a grading rubric (use concrete, specific criteria. format it as a table). Then, you are done. Ask me if there is anything else I want. [Step 4] Keep going until I say I am satisfied with one of your options. Then, provide me with a full outline of the assignment. This will include a full write-up of the assignment for students and a grading rubric (use concrete, specific criteria. format it as a table). Then, you are done. Ask me if there is anything else I want. [Details] When generating the alternative assignments, you will stick as close as possible to the principles of Project-Based Learning (PBL). This means creating an assignment that is constructive, collaborative, contextual, self-directed, and flexible. Essentially, it should invite students to own their own learning and apply course principles to a personal project or passion.

If you haven't played with AI sound creation then there's never been a better time to start. Udio.com is leveling up their game and of course Suno.com is one of the most fun tools to play with. My buddy Helix is always creating short little songs and surprising his friends with them a few minutes later! Enhance Your Teaching with Udio v1.5: New Lesson Plans, Resources, and Timetable Improvements Udio recently announced the release of Udio v1.5, bringing a suite of upgrades to their platform. This new version aims to enhance user experience and integrate new functionalities tailored towards optimizing the learning journey. According to Udio, the update introduces 'Lesson Plans' and 'Resources,' which allow educators to curate and manage lesson materials more efficiently. Additionally, the 'Timetable' feature has been refined to simplify scheduling for both teachers and students. Udio has also improved its user interface for better accessibility and ease of navigation. The company's focus on making learning more personalized and streamlined is evident through their emphasis on user-friendly tools. For instance, the 'Lesson Plans' feature enables educators to share templates and reuse them, saving significant preparation time. Furthermore, the new 'Resources' section allows the inclusion of various types of content, such as videos and quizzes, into lesson plans, fostering a more engaging learning atmosphere. The refined 'Timetable' feature makes it straightforward to manage classes, avoiding conflicts and ensuring clear communication. While the updates bring notable advantages, there are some potential drawbacks. One possible downside could be the learning curve associated with new features. Educators accustomed to the old version may need time to adapt to the new functionalities. Additionally, over-reliance on automated tools could inadvertently reduce the personalized touch of teaching. POSSIBLE BUSINESS USE CASES * Develop a platform that integrates Udio's new 'Lesson Plans' feature with artificial intelligence to recommend and customize lesson templates based on student data. * Create an education consultancy that specializes in helping schools and institutions fully leverage Udio's v1.5 update through personalized training and implementation services. * Launch a content creation service that designs engaging, multimedia-rich resources tailored to fit seamlessly with Udio's 'Resources' feature. As educators and technologists continue to innovate, how can we ensure that automated learning tools enrich the educational experience without diminishing the human element that is crucial to effective teaching? Read original article here: https://buff.ly/4c3Q7t0 Image Credit: DALL-E #UdioUpdate #EdTech #LearningInnovation #TeacherTools #EducationalTechnology

6 Redefining Personalized and Adaptive Learning for Educators According to Sipahioglu (2024), generative AI presents a transformative potential in education to empower educators through personalized learning, tailored assessments, and streamlined workflows. I would like to add that reframing personalized and adaptive learning for educators can be achieved through self-regulation, educator agency, and knowledge-building. Self-regulation refers to a learner's ability to manage their learning journey within the workplace environment. This includes goal setting, planning, time management, monitoring progress, knowing how and where to seek help from mentors, colleagues, or supervisors, and adapting to challenges. Educator agency emphasizes the educator’s active role in shaping their own learning experience and involves taking initiative, critical thinking, problem-solving, and having control over the types of work experiences they can participate in to maximize their learning, and critically reflecting on their experiences and identifying ways to improve their performance and understanding. Knowledge-building focuses on how educators actively construct and expand their knowledge base through work experiences and involves connecting theory to practice, building new knowledge, collaborative learning, critical reflection, or making sense of their experiences by analyzing problems and solutions encountered at work and developing transferable skills. This type of professional learning for educators can occur naturally through everyday activities like collaborating with experienced colleagues and observing work (designing, planning, teaching, and assessing) followed by evaluations and reflections on actual practice. These are forms of work-integrated learning (WIL) crucial for adopting new practices and driving workplace innovation. By advancing self-regulation, educator agency, and knowledge-building, WIL programs can empower educators to take charge of their professional development and become successful in the workplace. Generative AI can enhance educator agency by embracing ‘hybrid’ or ‘augmented’ intelligence where educator wisdom and experiences are combined with AI insights. By embracing hybrid intelligence, where AI complements human expertise, educators can leverage generative AI to enhance their roles in learning experience design, teaching, and assessment, ultimately benefiting from the insights and efficiencies offered by these technologies. Generative AI tools should enhance not replace, educator agency. In all of this, harnessing the power of data through learning analytics while ensuring that it is used responsibly and ethically to enhance student learning and well-being is critical. Sipahioglu, M. (2024). Empowering Teachers with Generative AI Tools and Support. In Transforming Education with Generative AI: Prompt Engineering and Synthetic Content Creation (pp. 214-238). IGI Global.

Brilliant way to begin exploring methods of mondernizing traditional assignment structures. The key is in testing and iterating.

**The Future of Online Learning: Artificial Intelligence as a Game Changer** As artificial intelligence (AI) rapidly transforms industries, its impact on education, particularly online learning, is paramount. The increasing demand for flexible, accessible, and personalized learning experiences suggests that AI is uniquely positioned to address these evolving needs. Professionals today are navigating dynamic markets; continuous learning has never been more essential. Technological advancements have made it possible to deliver educational content more efficiently, but the singular power AI contributes is personalization. By analyzing vast datasets, AI can cater lesson plans or even entire learning paths to individual users' preferences and competencies. AI-driven platforms adapt in real-time, providing students with precise feedback, dynamically adjusting material complexity, and even predicting learning gaps—leading to more engaged and effective education. Automation of administrative processes like grading and course management frees educators to focus on pedagogy and mentorship, optimizing time spent on high-value interactions. From a strategic perspective, companies that leverage AI in their learning and development programs can strengthen employee competencies while ensuring alignment with organizational needs. For educational institutions, adopting AI-based tools could revolutionize curriculum development, facilitating the design of future-proof programs that respond to global competence demands. However, while AI promises immense benefits, it also challenges stakeholders to rethink the ethics of data privacy, accessibility, and the role of human oversight in education. The integration of AI in online learning is not a distant possibility—it's actively reshaping how education is experienced today. The question now is how quickly institutions and organizations can adapt to this inevitable shift. **How do you think AI will shape the future of education? Share your thoughts in the comments below.**